Overview

Dataset statistics

Number of variables13
Number of observations379
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.6 KiB
Average record size in memory104.3 B

Variable types

Numeric12
Categorical1

Warnings

CRIM is highly correlated with RAD and 1 other fieldsHigh correlation
ZN is highly correlated with INDUS and 3 other fieldsHigh correlation
INDUS is highly correlated with ZN and 6 other fieldsHigh correlation
NOX is highly correlated with ZN and 6 other fieldsHigh correlation
RM is highly correlated with LSTATHigh correlation
AGE is highly correlated with ZN and 5 other fieldsHigh correlation
DIS is highly correlated with ZN and 5 other fieldsHigh correlation
RAD is highly correlated with CRIM and 4 other fieldsHigh correlation
TAX is highly correlated with CRIM and 6 other fieldsHigh correlation
LSTAT is highly correlated with INDUS and 6 other fieldsHigh correlation
CRIM is highly correlated with ZN and 7 other fieldsHigh correlation
ZN is highly correlated with CRIM and 4 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 6 other fieldsHigh correlation
NOX is highly correlated with CRIM and 7 other fieldsHigh correlation
RM is highly correlated with LSTATHigh correlation
AGE is highly correlated with CRIM and 6 other fieldsHigh correlation
DIS is highly correlated with CRIM and 6 other fieldsHigh correlation
RAD is highly correlated with CRIM and 2 other fieldsHigh correlation
TAX is highly correlated with CRIM and 6 other fieldsHigh correlation
LSTAT is highly correlated with CRIM and 6 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 4 other fieldsHigh correlation
ZN is highly correlated with INDUS and 1 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 4 other fieldsHigh correlation
NOX is highly correlated with CRIM and 4 other fieldsHigh correlation
AGE is highly correlated with NOX and 1 other fieldsHigh correlation
DIS is highly correlated with CRIM and 3 other fieldsHigh correlation
RAD is highly correlated with CRIM and 1 other fieldsHigh correlation
TAX is highly correlated with CRIM and 2 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 4 other fieldsHigh correlation
DIS is highly correlated with INDUS and 7 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 8 other fieldsHigh correlation
RAD is highly correlated with DIS and 7 other fieldsHigh correlation
LSTAT is highly correlated with CRIM and 8 other fieldsHigh correlation
AGE is highly correlated with DIS and 6 other fieldsHigh correlation
B is highly correlated with CRIM and 2 other fieldsHigh correlation
NOX is highly correlated with CRIM and 8 other fieldsHigh correlation
TAX is highly correlated with DIS and 5 other fieldsHigh correlation
RM is highly correlated with CRIM and 3 other fieldsHigh correlation
ZN is highly correlated with DIS and 6 other fieldsHigh correlation
PTRATIO is highly correlated with DIS and 8 other fieldsHigh correlation
ZN has 280 (73.9%) zeros Zeros

Reproduction

Analysis started2021-06-04 17:28:35.985492
Analysis finished2021-06-04 17:28:46.333045
Duration10.35 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

CRIM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct377
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.61310628
Minimum0.01301
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:46.396237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01301
5-th percentile0.03034
Q10.082325
median0.25356
Q33.630895
95-th percentile15.60416
Maximum88.9762
Range88.96319
Interquartile range (IQR)3.54857

Descriptive statistics

Standard deviation9.010515446
Coefficient of variation (CV)2.493841794
Kurtosis38.89161158
Mean3.61310628
Median Absolute Deviation (MAD)0.21588
Skewness5.449813532
Sum1369.36728
Variance81.18938859
MonotonicityNot monotonic
2021-06-04T10:28:46.479818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.33372
 
0.5%
0.015012
 
0.5%
0.578341
 
0.3%
0.037051
 
0.3%
5.731161
 
0.3%
13.35981
 
0.3%
0.066171
 
0.3%
0.211241
 
0.3%
4.898221
 
0.3%
0.251991
 
0.3%
Other values (367)367
96.8%
ValueCountFrequency (%)
0.013011
0.3%
0.013811
0.3%
0.014321
0.3%
0.014391
0.3%
0.015012
0.5%
0.015381
0.3%
0.017091
0.3%
0.01871
0.3%
0.019511
0.3%
0.020091
0.3%
ValueCountFrequency (%)
88.97621
0.3%
73.53411
0.3%
67.92081
0.3%
45.74611
0.3%
41.52921
0.3%
38.35181
0.3%
28.65581
0.3%
25.94061
0.3%
25.04611
0.3%
24.80171
0.3%

ZN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.75725594
Minimum0
Maximum100
Zeros280
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:46.556564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation22.41265631
Coefficient of variation (CV)2.083491965
Kurtosis4.517109163
Mean10.75725594
Median Absolute Deviation (MAD)0
Skewness2.310886144
Sum4077
Variance502.3271628
MonotonicityNot monotonic
2021-06-04T10:28:46.628622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0280
73.9%
2015
 
4.0%
8012
 
3.2%
229
 
2.4%
12.58
 
2.1%
258
 
2.1%
405
 
1.3%
305
 
1.3%
214
 
1.1%
334
 
1.1%
Other values (15)29
 
7.7%
ValueCountFrequency (%)
0280
73.9%
12.58
 
2.1%
17.51
 
0.3%
2015
 
4.0%
214
 
1.1%
229
 
2.4%
258
 
2.1%
282
 
0.5%
305
 
1.3%
334
 
1.1%
ValueCountFrequency (%)
1001
 
0.3%
951
 
0.3%
903
 
0.8%
852
 
0.5%
82.51
 
0.3%
8012
3.2%
752
 
0.5%
703
 
0.8%
603
 
0.8%
551
 
0.3%

INDUS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16751979
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:46.707126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.875301296
Coefficient of variation (CV)0.6156515883
Kurtosis-1.17240558
Mean11.16751979
Median Absolute Deviation (MAD)6.28
Skewness0.3226806286
Sum4232.49
Variance47.26976791
MonotonicityNot monotonic
2021-06-04T10:28:46.787121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.196
25.3%
19.5823
 
6.1%
8.1415
 
4.0%
6.215
 
4.0%
21.8911
 
2.9%
9.99
 
2.4%
3.979
 
2.4%
5.869
 
2.4%
10.598
 
2.1%
8.568
 
2.1%
Other values (58)176
46.4%
ValueCountFrequency (%)
0.461
 
0.3%
0.741
 
0.3%
1.211
 
0.3%
1.252
0.5%
1.321
 
0.3%
1.381
 
0.3%
1.523
0.8%
1.691
 
0.3%
1.891
 
0.3%
1.912
0.5%
ValueCountFrequency (%)
27.745
 
1.3%
25.655
 
1.3%
21.8911
 
2.9%
19.5823
 
6.1%
18.196
25.3%
15.042
 
0.5%
13.925
 
1.3%
13.893
 
0.8%
12.835
 
1.3%
11.934
 
1.1%

CHAS
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
0.0
356 
1.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0356
93.9%
1.023
 
6.1%

Length

2021-06-04T10:28:46.943128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-04T10:28:46.989939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0356
93.9%
1.023
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0735
64.6%
.379
33.3%
123
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number758
66.7%
Other Punctuation379
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0735
97.0%
123
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1137
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0735
64.6%
.379
33.3%
123
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0735
64.6%
.379
33.3%
123
 
2.0%

NOX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5547596306
Minimum0.392
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.045538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.392
5-th percentile0.4109
Q10.453
median0.538
Q30.624
95-th percentile0.743
Maximum0.871
Range0.479
Interquartile range (IQR)0.171

Descriptive statistics

Standard deviation0.1156828559
Coefficient of variation (CV)0.2085278913
Kurtosis-0.05337746777
Mean0.5547596306
Median Absolute Deviation (MAD)0.086
Skewness0.7427763672
Sum210.2539
Variance0.01338252315
MonotonicityNot monotonic
2021-06-04T10:28:47.136943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53815
 
4.0%
0.71315
 
4.0%
0.69314
 
3.7%
0.48912
 
3.2%
0.43712
 
3.2%
0.87112
 
3.2%
0.60511
 
2.9%
0.62411
 
2.9%
0.50710
 
2.6%
0.4319
 
2.4%
Other values (68)258
68.1%
ValueCountFrequency (%)
0.3922
 
0.5%
0.3941
 
0.3%
0.3981
 
0.3%
0.43
0.8%
0.4013
0.8%
0.4042
 
0.5%
0.4052
 
0.5%
0.4093
0.8%
0.412
 
0.5%
0.4115
1.3%
ValueCountFrequency (%)
0.87112
3.2%
0.777
1.8%
0.746
 
1.6%
0.7185
 
1.3%
0.71315
4.0%
0.78
2.1%
0.69314
3.7%
0.6796
 
1.6%
0.6715
 
1.3%
0.6681
 
0.3%

RM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct349
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.283593668
Minimum3.561
Maximum8.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.226608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.2603
Q15.89
median6.195
Q36.6185
95-th percentile7.6176
Maximum8.725
Range5.164
Interquartile range (IQR)0.7285

Descriptive statistics

Standard deviation0.7137077296
Coefficient of variation (CV)0.1135827311
Kurtosis1.890679648
Mean6.283593668
Median Absolute Deviation (MAD)0.336
Skewness0.3826854739
Sum2381.482
Variance0.5093787233
MonotonicityNot monotonic
2021-06-04T10:28:47.318065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2293
 
0.8%
6.1673
 
0.8%
6.7822
 
0.5%
5.8542
 
0.5%
5.9362
 
0.5%
6.7942
 
0.5%
6.1442
 
0.5%
6.1272
 
0.5%
5.3042
 
0.5%
6.9682
 
0.5%
Other values (339)357
94.2%
ValueCountFrequency (%)
3.5611
0.3%
3.8631
0.3%
4.1381
0.3%
4.5191
0.3%
4.6281
0.3%
4.6521
0.3%
4.881
0.3%
4.9031
0.3%
4.9061
0.3%
4.9261
0.3%
ValueCountFrequency (%)
8.7251
0.3%
8.7041
0.3%
8.3981
0.3%
8.3751
0.3%
8.3371
0.3%
8.2971
0.3%
8.2661
0.3%
8.2591
0.3%
8.2471
0.3%
8.0691
0.3%

AGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct287
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5883905
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.409209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17.68
Q143.9
median77.3
Q393.7
95-th percentile100
Maximum100
Range94
Interquartile range (IQR)49.8

Descriptive statistics

Standard deviation28.20050794
Coefficient of variation (CV)0.4111557033
Kurtosis-0.948976521
Mean68.5883905
Median Absolute Deviation (MAD)18.9
Skewness-0.6236126665
Sum25995
Variance795.2686479
MonotonicityNot monotonic
2021-06-04T10:28:47.495510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10033
 
8.7%
964
 
1.1%
87.94
 
1.1%
98.84
 
1.1%
97.43
 
0.8%
95.63
 
0.8%
973
 
0.8%
95.43
 
0.8%
883
 
0.8%
76.53
 
0.8%
Other values (277)316
83.4%
ValueCountFrequency (%)
61
0.3%
6.21
0.3%
6.51
0.3%
6.61
0.3%
6.81
0.3%
7.81
0.3%
8.41
0.3%
8.91
0.3%
9.81
0.3%
9.91
0.3%
ValueCountFrequency (%)
10033
8.7%
99.31
 
0.3%
99.11
 
0.3%
98.92
 
0.5%
98.84
 
1.1%
98.71
 
0.3%
98.51
 
0.3%
98.41
 
0.3%
98.31
 
0.3%
98.22
 
0.5%

DIS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct321
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.776123747
Minimum1.1691
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.585391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1691
5-th percentile1.48736
Q12.10035
median3.1025
Q35.1167
95-th percentile7.9549
Maximum12.1265
Range10.9574
Interquartile range (IQR)3.01635

Descriptive statistics

Standard deviation2.106978124
Coefficient of variation (CV)0.5579738021
Kurtosis0.7081456303
Mean3.776123747
Median Absolute Deviation (MAD)1.2524
Skewness1.074516209
Sum1431.1509
Variance4.439356815
MonotonicityNot monotonic
2021-06-04T10:28:47.672633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.81474
 
1.1%
3.49524
 
1.1%
5.11673
 
0.8%
4.72113
 
0.8%
6.33613
 
0.8%
5.40073
 
0.8%
6.4983
 
0.8%
4.81223
 
0.8%
3.94543
 
0.8%
3.65193
 
0.8%
Other values (311)347
91.6%
ValueCountFrequency (%)
1.16911
0.3%
1.17421
0.3%
1.17811
0.3%
1.20241
0.3%
1.32161
0.3%
1.33251
0.3%
1.34591
0.3%
1.3581
0.3%
1.38612
0.5%
1.41181
0.3%
ValueCountFrequency (%)
12.12651
0.3%
10.71031
0.3%
10.58572
0.5%
9.22291
0.3%
9.22032
0.5%
9.18761
0.3%
8.90672
0.5%
8.79212
0.5%
8.53531
0.3%
8.3441
0.3%

RAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.461741425
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.745009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.599279417
Coefficient of variation (CV)0.9088474341
Kurtosis-0.7757843183
Mean9.461741425
Median Absolute Deviation (MAD)1
Skewness1.044154395
Sum3586
Variance73.94760648
MonotonicityNot monotonic
2021-06-04T10:28:47.810554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2496
25.3%
485
22.4%
584
22.2%
324
 
6.3%
621
 
5.5%
820
 
5.3%
219
 
5.0%
716
 
4.2%
114
 
3.7%
ValueCountFrequency (%)
114
 
3.7%
219
 
5.0%
324
 
6.3%
485
22.4%
584
22.2%
621
 
5.5%
716
 
4.2%
820
 
5.3%
2496
25.3%
ValueCountFrequency (%)
2496
25.3%
820
 
5.3%
716
 
4.2%
621
 
5.5%
584
22.2%
485
22.4%
324
 
6.3%
219
 
5.0%
114
 
3.7%

TAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406.2823219
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:47.886259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.2674301
Coefficient of variation (CV)0.4141637994
Kurtosis-1.102595217
Mean406.2823219
Median Absolute Deviation (MAD)73
Skewness0.6999476326
Sum153981
Variance28313.92802
MonotonicityNot monotonic
2021-06-04T10:28:47.976220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66696
25.3%
30730
 
7.9%
40323
 
6.1%
43711
 
2.9%
30410
 
2.6%
2649
 
2.4%
3309
 
2.4%
2778
 
2.1%
4328
 
2.1%
3848
 
2.1%
Other values (50)167
44.1%
ValueCountFrequency (%)
1871
 
0.3%
1885
1.3%
1936
1.6%
1981
 
0.3%
2163
0.8%
2226
1.6%
2234
1.1%
2247
1.8%
2335
1.3%
2422
 
0.5%
ValueCountFrequency (%)
7115
 
1.3%
66696
25.3%
43711
 
2.9%
4328
 
2.1%
4303
 
0.8%
4221
 
0.3%
4111
 
0.3%
40323
 
6.1%
3987
 
1.8%
3916
 
1.6%

PTRATIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.45540897
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:48.060849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.140140684
Coefficient of variation (CV)0.1159627883
Kurtosis-0.2231730165
Mean18.45540897
Median Absolute Deviation (MAD)1.2
Skewness-0.8049313636
Sum6994.6
Variance4.580202147
MonotonicityNot monotonic
2021-06-04T10:28:48.142828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
20.2102
26.9%
14.725
 
6.6%
17.820
 
5.3%
2119
 
5.0%
17.415
 
4.0%
19.215
 
4.0%
19.114
 
3.7%
16.613
 
3.4%
18.613
 
3.4%
18.413
 
3.4%
Other values (33)130
34.3%
ValueCountFrequency (%)
12.62
 
0.5%
139
 
2.4%
13.61
 
0.3%
14.41
 
0.3%
14.725
6.6%
14.83
 
0.8%
14.92
 
0.5%
15.11
 
0.3%
15.27
 
1.8%
15.51
 
0.3%
ValueCountFrequency (%)
222
 
0.5%
21.211
 
2.9%
2119
 
5.0%
20.98
 
2.1%
20.2102
26.9%
20.15
 
1.3%
19.77
 
1.8%
19.66
 
1.6%
19.215
 
4.0%
19.114
 
3.7%

B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct273
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.7154881
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:48.241021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile87.788
Q1376.715
median392.18
Q3396.22
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)19.505

Descriptive statistics

Standard deviation91.54343339
Coefficient of variation (CV)0.2559112938
Kurtosis7.275504168
Mean357.7154881
Median Absolute Deviation (MAD)4.72
Skewness-2.906162192
Sum135574.17
Variance8380.200196
MonotonicityNot monotonic
2021-06-04T10:28:48.329767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.990
 
23.7%
395.62
 
0.5%
395.692
 
0.5%
395.632
 
0.5%
395.562
 
0.5%
395.242
 
0.5%
393.682
 
0.5%
396.062
 
0.5%
391.342
 
0.5%
393.232
 
0.5%
Other values (263)271
71.5%
ValueCountFrequency (%)
0.321
0.3%
2.521
0.3%
3.51
0.3%
3.651
0.3%
6.681
0.3%
7.681
0.3%
10.481
0.3%
16.451
0.3%
21.571
0.3%
22.011
0.3%
ValueCountFrequency (%)
396.990
23.7%
396.421
 
0.3%
396.31
 
0.3%
396.281
 
0.3%
396.241
 
0.3%
396.231
 
0.3%
396.212
 
0.5%
396.141
 
0.3%
396.062
 
0.5%
395.991
 
0.3%

LSTAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct356
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.77015831
Minimum1.73
Maximum36.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2021-06-04T10:28:48.422243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.588
Q17.13
median11.45
Q317.115
95-th percentile27.272
Maximum36.98
Range35.25
Interquartile range (IQR)9.985

Descriptive statistics

Standard deviation7.182040098
Coefficient of variation (CV)0.5624080707
Kurtosis0.4149520109
Mean12.77015831
Median Absolute Deviation (MAD)4.83
Skewness0.8993281776
Sum4839.89
Variance51.58169997
MonotonicityNot monotonic
2021-06-04T10:28:48.507652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.053
 
0.8%
18.133
 
0.8%
15.172
 
0.5%
7.442
 
0.5%
8.12
 
0.5%
6.362
 
0.5%
12.432
 
0.5%
9.972
 
0.5%
5.982
 
0.5%
3.952
 
0.5%
Other values (346)357
94.2%
ValueCountFrequency (%)
1.731
0.3%
1.981
0.3%
2.471
0.3%
2.871
0.3%
2.881
0.3%
2.941
0.3%
2.961
0.3%
2.971
0.3%
3.112
0.5%
3.162
0.5%
ValueCountFrequency (%)
36.981
0.3%
34.771
0.3%
34.411
0.3%
34.371
0.3%
34.021
0.3%
31.991
0.3%
30.811
0.3%
30.621
0.3%
30.591
0.3%
29.971
0.3%

Interactions

2021-06-04T10:28:36.364601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.429851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.493506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.556464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.618057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.678217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.738344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.799700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.861280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.923318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:36.983637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:37.051123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-06-04T10:28:45.362492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.429678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.498583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.566864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.635411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.702728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-04T10:28:45.964256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-04T10:28:48.592877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-04T10:28:48.716524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-04T10:28:48.841763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-04T10:28:48.967199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-04T10:28:46.107084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-04T10:28:46.278399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
00.039610.05.190.00.5156.03734.55.98535.0224.020.2396.908.01
13.321050.019.581.00.8715.403100.01.32165.0403.014.7396.9026.82
21.207420.019.580.00.6055.87594.62.42595.0403.014.7292.2914.43
30.1061230.04.930.00.4286.09565.16.33616.0300.016.6394.6212.40
417.866700.018.100.00.6716.223100.01.386124.0666.020.2393.7421.78
513.522200.018.100.00.6313.863100.01.510624.0666.020.2131.4213.33
614.333700.018.100.00.7004.880100.01.589524.0666.020.2372.9230.62
72.446680.019.580.00.8715.27294.01.73645.0403.014.788.6316.14
85.581070.018.100.00.7136.43687.92.315824.0666.020.2100.1916.22
91.354720.08.140.00.5386.072100.04.17504.0307.021.0376.7313.04

Last rows

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
3690.150380.025.650.00.5815.85697.01.94442.0188.019.1370.3125.41
3700.171200.08.560.00.5205.83691.92.21105.0384.020.9395.6718.66
3710.0355125.04.860.00.4266.16746.75.40074.0281.019.0390.647.51
3720.784200.08.140.00.5385.99081.74.25794.0307.021.0386.7514.67
3730.537000.06.200.00.5045.98168.13.67158.0307.017.4378.3511.65
3740.081870.02.890.00.4457.82036.93.49522.0276.018.0393.533.57
3754.871410.018.100.00.6146.48493.62.305324.0666.020.2396.2118.68
3760.351140.07.380.00.4936.04149.94.72115.0287.019.6396.907.70
3779.187020.018.100.00.7005.536100.01.580424.0666.020.2396.9023.60
3784.555870.018.100.00.7183.56187.91.613224.0666.020.2354.707.12